CMR quantitation of change in mitral regurgitation following transcatheter aortic valve replacement (TAVR): impact on left ventricular reverse remodeling and outcome

Clinical impact of baseline mitral regurgitation on outcomes after transcatheter aortic valve replacement for severe aortic stenosis

Objective

The clinical impact of baseline mitral regurgitation (MR) on the outcomes after transcatheter aortic valve replacement (TAVR) is not clear. This study sought to assess the clinical impact of baseline MR on outcomes after TAVR.

Methods

The study was a retrospective analysis. Data was from 120 consecutive patients with severe aortic stenosis (AS) undergoing TAVR at our center from June 2018 and July 2020. Clinical outcomes were assessed at 30-day, 1- and 2-year follow-up.

Results

The median follow-up was 736.0 (interquartile range, 666.0-965.0) days. Overall survival in patients with nonsignificant and significant baseline MR was not significantly different, while patients from the improved MR group after TAVR demonstrated a significantly higher survival than unchanged or worsened MR group during 2-year follow-up. NYHA functional class had generally improved at 1 year, with only 8.3 % of patients with nonsignificant MR and 17.5 % of patients with significant MR in class III or IV. Patients with improved MR at 1 year after TAVR had a significantly higher LVEF, smaller LVEDD and LVESD than those with unchanged or worsened MR. Among the significant baseline MR group, 70.4 % and 80.0 % of patients had improved to nonsignificant MR at 30-day and 1-year follow-up after TAVR, respectively.

Conclusions

Significant baseline MR was not associated with the increased risk of all-cause mortality 2 years after TAVR. Significant baseline MR was improved in most patients at 1 year after TAVR. Patients with unchanged or worsened MR had an increased all-cause mortality.

Artificial Intelligence framework with traditional computer vision and deep learning approaches for optimal automatic segmentation of left ventricle with scar

Automatic segmentation of the cardiac left ventricle with scars remains a challenging and clinically significant task, as it is essential for patient diagnosis and treatment pathways. This study aimed to develop a novel framework and cost function to achieve optimal automatic segmentation of the left ventricle with scars using LGE-MRI images. To ensure the generalization of the framework, an unbiased validation protocol was established using out-of-distribution (OOD) internal and external validation cohorts, and intra-observation and inter-observer variability ground truths. The framework employs a combination of traditional computer vision techniques and deep learning, to achieve optimal segmentation results. The traditional approach uses multi-atlas techniques, active contours, and k-means methods, while the deep learning approach utilizes various deep learning techniques and networks. The study found that the traditional computer vision technique delivered more accurate results than deep learning, except in cases where there was breath misalignment error. The optimal solution of the framework achieved robust and generalized results with Dice scores of 82.8 ± 6.4% and 72.1 ± 4.6% in the internal and external OOD cohorts, respectively. The developed framework offers a high-performance solution for automatic segmentation of the left ventricle with scars using LGE-MRI. Unlike existing state-of-the-art approaches, it achieves unbiased results across different hospitals and vendors without the need for training or tuning in hospital cohorts. This framework offers a valuable tool for experts to accomplish the task of fully automatic segmentation of the left ventricle with scars based on a single-modality cardiac scan.

Automatic development of 3D anatomical models of border zone and core scar regions in the left ventricle

Patients with myocardial infarction are at elevated risk of sudden cardiac death, and scar tissue arising from infarction is known to play a role. The accurate identification of scars therefore is crucial for risk assessment, quantification and guiding interventions. Typically, core scars and grey peripheral zones are identified by radiologists and clinicians based on cardiac late gadolinium enhancement magnetic resonance images (LGE-MRI). Scar regions from LGE-MRI vary in size, shape, heterogeneity, artifacts, and image resolution. Thus, manual segmentation is time consuming, and influenced by the observer's experience (bias effect). We propose a fully automatic framework that develops 3D anatomical models of the left ventricle with border zone and core scar regions that are free from bias effect. Our myocardium (SOCRATIS), border scar and core scar (BZ-SOCRATIS) segmentation pipelines were evaluated using internal and external validation datasets. The automatic myocardium segmentation framework performed a Dice score of 81.9% and 70.0% in the internal and external validation dataset. The automatic scar segmentation pipeline achieved a Dice score of 60.9% for the core scar segmentation and 43.7% for the border zone scar segmentation in the internal dataset and in the external dataset a Dice score of 44.2% for the core scar segmentation and 54.8% for the border scar segmentation respectively. To the best of our knowledge, this is the first study outlining a fully automatic framework to develop 3D anatomical models of the left ventricle with border zone and core scar regions. Our method exhibits high performance without the need for training or tuning in an unseen cohort (unsupervised).

Mitral regurgitation quantified by CMR 4D-flow is associated with microvascular obstruction post reperfused ST-segment elevation myocardial infarction

Objectives

Mitral regurgitation (MR) and microvascular obstruction (MVO) are common complications of myocardial infarction (MI). This study aimed to investigate the association between MR in ST-elevation MI (STEMI) subjects with MVO post-reperfusion. STEMI subjects undergoing primary percutaneous intervention were enrolled. Cardiovascular magnetic resonance (CMR) imaging was performed within 48-hours of initial presentation. 4D flow images of CMR were analysed using a retrospective valve tracking technique to quantify MR volume, and late gadolinium enhancement images of CMR to assess MVO.

Results

Among 69 patients in the study cohort, 41 had MVO (59%). Patients with MVO had lower left ventricular (LV) ejection fraction (EF) (42 ± 10% vs. 52 ± 8%, P < 0.01), higher end-systolic volume (98 ± 49 ml vs. 73 ± 28 ml, P < 0.001) and larger scar volume (26 ± 19% vs. 11 ± 9%, P < 0.001). Extent of MVO was associated with the degree of MR quantified by 4D flow (R = 0.54, P = 0.0003). In uni-variate regression analysis, investigating the association of CMR variables to the degree of acute MR, only the extent of MVO was associated (coefficient = 0.27, P = 0.001). The area under the curve for the presence of MVO was 0.66 (P = 0.01) for MR > 2.5 ml. We conclude that in patients with reperfused STEMI, the degree of acute MR is associated with the degree of MVO.

Review: application of current imaging modalities in the management of left-sided valvular heart disease

In terms of valvular heart disease (VHD) imaging, transthoracic echocardiography (TTE) is the preferred first choice because of its widespread availability. Other modalities, such as transesophageal echocardiography, computed tomography and magnetic resonance imaging, have played a supplementary role in diagnosis for severity, deciding the timing/type of treatment, detection of post procedural complications, and prognostic predictions. However, there are few consensuses on how to employ these modalities, as the evidence is not extensive as that for TTE. On the other hand, these imaging modalities also have their own unique strengths. If employed properly, these modalities have the potential to play a more prominent role in clinical decision making. In this review, we focus on the potential, limitations and application of current imaging modalities in the management of left-sided VHD.

Standard and emerging CMR methods for mitral regurgitation quantification

BACKGROUND

There are several methods to quantify mitral regurgitation (MR) by cardiovascular magnetic resonance (CMR). The interoperability of these methods and their reproducibility remains undetermined.

OBJECTIVE

To determine the agreement and reproducibility of different MR quantification methods by CMR across all aetiologies.

METHODS

Thirty-five patients with MR were recruited (primary MR = 12, secondary MR = 10 and MVR = 13). Patients underwent CMR, including cines and four-dimensional flow (4D flow). Four methods were evaluated: MR_Standard (left ventricular stroke volume - aortic forward flow by phase contrast), MR_LVRV (left ventricular stroke volume - right ventricular stroke volume), MR_Jet (direct jet quantification by 4D flow) and MR_MVAV (mitral forward flow by 4D flow - aortic forward flow by 4D flow). For all cases and MR types, 520 MR volumes were recorded by these 4 methods for intra-/inter-observer tests.

RESULTS

In primary MR, MR_MVAV and MR_LVRV were comparable to MR_Standard (P > 0.05). MR_Jet resulted in significantly higher MR volumes when compared to MR_Standard (P < 0.05) In secondary MR and MVR cases, all methods were comparable. In intra-observer tests, MR_MVAV demonstrated least bias with best limits of agreement (bias = -0.1 ml, -8 ml to 7.8 ml, P = 0.9) and best concordance correlation coefficient (CCC = 0.96, P < 0.01). In inter-observer tests, for primary MR and MVR, least bias and highest CCC were observed for MR_MVAV. For secondary MR, bias was lowest for MR_Jet (-0.1 ml, PNS).

CONCLUSION

CMR methods of MR quantification demonstrate agreement in secondary MR and MVR. In primary MR, this was not observed. Across all types of MR, MR_MVAV quantification demonstrated the highest reproducibility and consistency.

Cardiac magnetic resonance systematically overestimates mitral regurgitations by the indirect method

Objective

Cardiac MRI is quickly emerging as the gold standard for assessment of mitral regurgitation, most commonly with the indirect method subtracting forward flow in aorta from volumetric segmentation of the left ventricle. We aimed to investigate how aortic flow measurements with increasing distance from the aortic valve affect calculated mitral regurgitations and whether measurements were influenced by breath-hold regimen.

Methods

Free-breathing and breath-hold phase contrast flows were measured in aorta at valve level, sinotubular (ST) junction, mid-ascending aorta and in the pulmonary trunk. Flow measurements were pairwise compared, and subsequently, after exclusion of patients with visible mitral and tricuspid regurgitations for left-sided and right-sided comparisons, respectively, flow-measured stroke volumes were compared with ventricular volumetric segmentations.

Results

Thirty-nine participants without arrhythmias or structural abnormalities of the large vessels were included. Stroke volumes measured with free-breathing and breath-hold flow decreased equally with increasing distance to the aortic valves (breath-hold flow: aortic valve 105.6±20.8 mL, ST junction 101.5±20.7 mL, mid-ascending aorta 98.1±21.5 mL). After exclusion of atrioventricular regurgitations, stroke volumes determined by volumetric measurements were higher compared with values determined by flow measurements, corresponding to ‘false’ atrioventricular regurgitations of 8.0%±5.8% with flow measured at valve level, 11.6%±5.2% at the ST junction and 15.3%±5.0% at the mid-ascending aorta.

Conclusions

Stroke volumes determined by flow decrease throughout the proximal aorta and are systematically lower than volumetrically measured stroke volumes. The indirect method systematically overestimates mitral regurgitations, especially with increasing distance from the aortic valves.

A validated computational framework to predict outcomes in TAVI

Transcatheter aortic valve implantation (TAVI) still presents complications: paravalvular leakage (PVL) and onset of conduction abnormalities leading to permanent pacemaker implantation. Our aim was testing a validated patient-specific computational framework for prediction of TAVI outcomes and possible complications. Twenty-eight TAVI patients (14 SapienXT and 14 CoreValve) were retrospectively selected. Pre-procedural CT images were post-processed to create 3D patient-specific implantation sites. The procedures were simulated with finite element analysis. Simulations' results were compared against post-procedural clinical fluoroscopy and echocardiography images. The computational model was in good agreement with clinical findings: the overall stent diameter difference was 2.6% and PVL was correctly identified with a post-processing algorithm in 83% of cases. Strains in the implantation site were studied to assess the risk of conduction system disturbance and were found highest in the patient who required pacemaker implantation. This study suggests that computational tool could support safe planning and broadening of TAVI.